Stochastic local search for large-scale instances of the Haplotype Inference Problem by Parsimony

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods that, at present, are adequate only for moderate size instances. In this paper, we present an approach based on the combination of local search metaheuristics and a reduction procedure based on an analysis of the problem structure. Results on a set of Haplotype Inference benchmarks show that this approach achieves a good trade-off between solution quality and execution time.